Multi-Omics Integration Lab

Patient Sample

Patient A — balanced baseline

Layer Weights

🧬 Genomics 25%

📜 Transcriptomics 25%

🧪 Proteomics 25%

⚗️ Metabolomics 25%

Correlation edge threshold 0.55

Integrated Readout

Integrated risk score

0.0

Dominant layer

—

Visible edges

Genomics nodes Transcriptomics nodes Proteomics nodes Metabolomics nodes

How it works

Each of the four molecular layers holds 10 measured entities (genes, transcripts, proteins, metabolites). For the current patient, every entity carries a z-score relative to a healthy reference population — how many standard deviations it deviates from normal. A gene, its transcript, the protein it encodes and a downstream metabolite are joined by a "central dogma" edge; within a layer, entities whose simulated correlation exceeds your threshold are also joined.

The panel computes a weighted multi-omics integration score, the same idea behind similarity-network-fusion approaches used in real precision-medicine pipelines:

R = 100 · Σ wᵢ·mean(|zᵢ|) / Σ wᵢ   for i ∈ {genomics, transcriptomics, proteomics, metabolomics}

Layer weight sliders — set how much each omics layer contributes to the fused risk score wᵢ; a layer at 0% is still shown but excluded from R.
Correlation edge threshold — only within-layer entity pairs whose simulated |correlation| exceeds this value draw a connecting edge, showing how tightening the threshold sparsifies each layer's sub-network.
Node color/size — hue marks the layer, node radius and glow scale with |z|, so outlier entities (large mutation burden, abnormal expression, elevated metabolite) stand out visually before any score is computed.
Next Patient — swaps in a different simulated omics profile (healthy baseline, an oncogenic signature skewed toward genomics/proteomics, and a metabolic-syndrome signature skewed toward proteomics/metabolomics) so you can see how the same weights produce very different fused scores depending on where the real biological signal sits.

Real-world relevance: oncology and rare-disease programs increasingly integrate genomic, transcriptomic, proteomic and metabolomic panels per patient rather than reading any single layer alone, because a single-omics test can miss a signal that only shows up as a correlated shift across layers.

Central-dogma link Correlation edge

⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.